深度學習伺服器配置 Ubuntu server 16.04+CUDA8.0+cuDNN7+tensorflow1.3
阿新 • • 發佈：2019-01-06
硬體配置
主機：Dell T620塔式伺服器
顯示卡：Nvidia Tesla K20c
系統&驅動配置
Ubuntu Server 16.04
Nvidia driver：375.26
CUDA 8.0
cuDNN 7.0.3
tensorflow1.3
1 ubuntu server系統安裝
系統：Ubuntu Server 16.04
安裝方式：光碟安裝（使用U盤安裝會出現ISO檔案無法掛載的問題）
光碟燒錄系統步驟：
- ISO檔案開啟方式選擇Windows光碟映像燒錄機
- 點選燒錄
1.1 網路
安裝後可能DHCP服務沒有啟動，需要手動啟動使用dhclient命令
使用ifconfig可以看到網絡卡資訊
[email protected]:~$ ifconfig
eno1 Link encap:Ethernet HWaddr f0:1f:af:e8:79:0e
UP BROADCAST MULTICAST MTU:1500 Metric:1
RX packets:0 errors:0 dropped:0 overruns:0 frame:0
TX packets:0 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:1000
RX bytes:0 ( 本伺服器的地址就是eno2網絡卡的地址
在校園網需要通過瀏覽器圖形介面登陸賬號密碼才能上網，使用以下命令代替
curl -X POST -F 'action=login' -F 'username=賬戶' -F 'password=密碼' -F 'ac_id=1' -F 'ajax=1' 1' http://10.0.10.66/include/auth_action.php
在使用前需要先能ping通10.0.10.66這個地址，在登陸後測試是否已經聯網
[email protected]:~$ curl www.baidu.com
<!DOCTYPE html>
<!--STATUS OK--><html> <head><meta http-equiv=content-type content=text/html;charset=utf-8><meta http-equiv=X-UA-Compatible content=IE=Edge><meta content=always name=referrer><link rel=stylesheet type=text/css href=http://s1.bdstatic.com/r/www/cache/bdorz/baidu.min.css><title>百度一下，你就知道</title></head> <body link=#0000cc> <div id=wrapper> <div id=head> <div class=head_wrapper> <div class=s_form> <div class=s_form_wrapper> <div id=lg> <img hidefocus=true src=//www.baidu.com/img/bd_logo1.png width=270 height=129> </div> <form id=form name=f action=//www.baidu.com/s class=fm> <input type=hidden name=bdorz_come value=1> <input type=hidden name=ie value=utf-8> <input type=hidden name=f value=8> <input type=hidden name=rsv_bp value=1> <input type=hidden name=rsv_idx value=1> <input type=hidden name=tn value=baidu><span class="bg s_ipt_wr"><input id=kw name=wd class=s_ipt value maxlength=255 autocomplete=off autofocus></span><span class="bg s_btn_wr"><input type=submit id=su value=百度一下 class="bg s_btn"></span> </form> </div> </div> <div id=u1> <a href=http://news.baidu.com name=tj_trnews class=mnav>新聞</a> <a href=http://www.hao123.com name=tj_trhao123 class=mnav>hao123</a> <a href=http://map.baidu.com name=tj_trmap class=mnav>地圖</a> <a href=http://v.baidu.com name=tj_trvideo class=mnav>視訊</a> <a href=http://tieba.baidu.com name=tj_trtieba class=mnav>貼吧</a> <noscript> <a href=http://www.baidu.com/bdorz/login.gif?login&tpl=mn&u=http%3A%2F%2Fwww.baidu.com%2f%3fbdorz_come%3d1 name=tj_login class=lb>登入</a> </noscript> <script>document.write('<a href="http://www.baidu.com/bdorz/login.gif?login&tpl=mn&u='+ encodeURIComponent(window.location.href+ (window.location.search === "" ? "?" : "&")+ "bdorz_come=1")+ '" name="tj_login" class="lb">登入</a>');</script> <a href=//www.baidu.com/more/ name=tj_briicon class=bri style="display: block;">更多產品</a> </div> </div> </div> <div id=ftCon> <div id=ftConw> <p id=lh> <a href=http://home.baidu.com>關於百度</a> <a href=http://ir.baidu.com>About Baidu</a> </p> <p id=cp>©2017 Baidu <a href=http://www.baidu.com/duty/>使用百度前必讀</a> <a href=http://jianyi.baidu.com/ class=cp-feedback>意見反饋</a> 京ICP證030173號 <img src=//www.baidu.com/img/gs.gif> </p> </div> </div> </div> </body> </html>
抓取了網站的內容，已經聯網
1.2 更換軟體源為清華大學
Ubuntu 的軟體源配置檔案是 /etc/apt/sources.list。將系統自帶的該檔案做個備份，將該檔案替換為下面內容，即可使用 TUNA 的軟體源映象。
$ sudo gedit /etc/apt/sources.list
修改如下：
# 預設註釋了原始碼映象以提高 apt update 速度，如有需要可自行取消註釋
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial main restricted universe multiverse
# deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial main restricted universe multiverse
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-updates main restricted universe multiverse
# deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-updates main restricted universe multiverse
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-backports main restricted universe multiverse
# deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-backports main restricted universe multiverse
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-security main restricted universe multiverse
# deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-security main restricted universe multiverse
# 預釋出軟體源，不建議啟用
# deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-proposed main restricted universe multiverse
# deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-proposed main restricted universe multiverse
修改後 sudo apt-get update 使修改生效
1.3 遠端登陸
安裝openssh： sudo apt-get install openssh-server
==================================================
檢視GPU使用情況： nvidia-smi
[email protected]:~$ nvidia-smi
Sat Sep 22 15:22:55 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.130 Driver Version: 384.130 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla K20c Off | 00000000:02:00.0 Off | 0 |
| 30% 33C P0 52W / 225W | 0MiB / 4742MiB | 98% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Ubuntu16.04 系統下K20c CUDA只能裝8.0以上版本
查詢結果
2 Pre-Installation Actions
[email protected]:~$ lspci | grep -i nvidia
02:00.0 3D controller: NVIDIA Corporation GK110GL [Tesla K20c] (rev a1)
[email protected]:~$ uname -m && cat /etc/*release
x86_64
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=16.04
DISTRIB_CODENAME=xenial
DISTRIB_DESCRIPTION="Ubuntu 16.04.3 LTS"
NAME="Ubuntu"
VERSION="16.04.3 LTS (Xenial Xerus)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 16.04.3 LTS"
VERSION_ID="16.04"
HOME_URL="http://www.ubuntu.com/"
SUPPORT_URL="http://help.ubuntu.com/"
BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/"
VERSION_CODENAME=xenial
UBUNTU_CODENAME=xenial
[email protected]:~$ gcc --version
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
[email protected]:~$ uname -r
4.4.0-87-generic
安裝對應的kernels header和開發包：
$ sudo apt-get install linux-headers-$(uname -r)
runfile 推薦 / deb /
3 CUDA 8.0 安裝
解除安裝CUDA相關包：
sudo apt-get remove cuda
sudo apt-get autoclean
sudo apt-get --purge remove nvidia* # 解除安裝Nvidia相關包
然後在目錄切換到/esr/local/下 cd /usr/local/
sudo rm -r cuda-*
3.1 runfile安裝
推薦使用runfile 方式安裝（deb方式解除安裝的時候麻煩）
可以用wget
wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run
sudo sh cuda_8.0.61_375.26_linux.run.26_linux-run
會出現一大堆選項，OPENGL安裝選no，其餘按照yes或者default選。如果已經安裝了新的驅動，不要選擇安裝驅動。
-------------------------------------------------------------
Do you accept the previously read EULA?
accept/decline/quit: accept
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 375.26?
(y)es/(n)o/(q)uit: n
Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: y
Enter Toolkit Location
[ default is /usr/local/cuda-8.0 ]:
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y
Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: y
Enter CUDA Samples Location
[ default is /home/cmfchina ]:
Installing the CUDA Toolkit in /usr/local/cuda-8.0 ...
Missing recommended library: libGLU.so
Missing recommended library: libX11.so
Missing recommended library: libXi.so
Missing recommended library: libXmu.so
Installing the CUDA Samples in /home/cmfchina ...
Copying samples to /home/cmfchina/NVIDIA_CUDA-8.0_Samples now...
Finished copying samples.
===========
= Summary =
===========
Driver: Not Selected
Toolkit: Installed in /usr/local/cuda-8.0
Samples: Installed in /home/cmfchina, but missing recommended libraries
Please make sure that
- PATH includes /usr/local/cuda-8.0/bin
- LD_LIBRARY_PATH includes /usr/local/cuda-8.0/lib64, or, add /usr/local/cuda-8.0/lib64 to /etc/ld.so.conf and run ldconfig as root
To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-8.0/bin
Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-8.0/doc/pdf for detailed information on setting up CUDA.
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 361.00 is required for CUDA 8.0 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
sudo <CudaInstaller>.run -silent -driver
3.2 設定環境變數
輸入命令，編輯環境變數配置檔案
sudo vim ~/.bashrc
在文字末端追加以下兩行程式碼（按鍵“i”進行編輯操作）
export PATH=/usr/local/cuda-8.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda
儲存退出，執行下面命令，使環境變數立刻生效
sudo source ~/.bashrc
sudo ldconfig
安裝完成後後重啟
3.3 檢查CUDA配置
[email protected]:/home/zjw# nvidia-smi
Sun Sep 23 17:31:45 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.26 Driver Version: 375.26 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla K20c Off | 0000:02:00.0 Off | 0 |
| 30% 32C P0 52W / 225W | 0MiB / 4742MiB | 98% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
檢查cuda是否配置正確
[email protected]:~$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Tue_Jan_10_13:22:03_CST_2017
Cuda compilation tools, release 8.0, V8.0.61
3.4 測試CUDA的sammples
進入cude sample code目錄，make 編譯所有demo。注意：因為這裡的make操作是將sample資料夾下所有的demo都編譯了一遍，所以比較好使，如果僅僅想測試某個例子，可以進入相應的資料夾去編譯即可。
# 切換到cuda-samples所在目錄
cd /usr/local/cuda-8.0/samples 或者 cd /home/NVIDIA_CUDA-8.0_Samples
# 沒有make，先安裝命令 sudo apt-get install cmake，-j是最大限度的使用cpu編譯，加快編譯的速度
make –j
# 編譯完畢，切換release目錄（/usr/local/cuda-8.0/samples/bin/x86_64/linux/release完整目錄）
cd ./bin/x86_64/linux/release
# 檢驗是否成功，執行例項
./deviceQuery
# 可以認真看看自行結果，它顯示了你的NVIDIA顯示卡的相關資訊，最後能看到Result = PASS就算成功。
編譯完成後切換到 bin 目錄
./deviceQuery
[email protected]:/home/zjw/CUDA_Samples/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/releas e# ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "Tesla K20c"
CUDA Driver Version / Runtime Version 8.0 / 8.0
CUDA Capability Major/Minor version number: 3.5
Total amount of global memory: 4742 MBytes (4972412928 bytes)
(13) Multiprocessors, (192) CUDA Cores/MP: 2496 CUDA Cores
GPU Max Clock rate: 706 MHz (0.71 GHz)
Memory Clock rate: 2600 Mhz
Memory Bus Width: 320-bit
L2 Cache Size: 1310720 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Enabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 2 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simu ltaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Versi on = 8.0, NumDevs = 1, Device0 = Tesla K20c
Result = PASS
輸出結果看到顯示卡相關資訊，並且最後Result = PASS ，這說明CUDA才真正完全安裝成功了
再檢查一下系統和CUDA-Capable device的連線情況
[email protected]:/home/zjw/CUDA_Samples/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release# ./bandwidthTest [CUDA Bandwidth Test] - Starting...
Running on...
Device 0: Tesla K20c
Quick Mode
Host to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 6160.9
Device to Host Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 6550.6
Device to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 146967.3
Result = PASS
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
4 cuDNN
官方安裝文件（照做基本不出問題）
4.1 下載cuDNN
cuDNN是GPU加速計算深層神經網路的庫。首先去官網(https://developer.nvidia.com/rdp/cudnn-download)下載cuDNN，需要註冊一個賬號才能下載，沒有的話自己註冊一個。由於本人的顯示卡是K20c，CUDA 8.0，最新的版本是v7：
下載速度幾KB。Nvidia把國內IP遮蔽了，建議代理換全域性模式，用國外IP就可以下載了，親測。
4.2 安裝cuDNN
安裝cudnn比較簡單，簡單地說，就是複製幾個檔案：庫檔案和標頭檔案。將cudnn的標頭檔案複製到cuda安裝路徑的include路徑下，將cudnn的庫檔案複製到cuda安裝路徑的lib64路徑下。具體操作如下
# 解壓檔案
[email protected]:~$ cp cudnn-8.0-linux-x64-v7.solitairetheme8 cudnn-8.0-linux-x64-v7.tgz
[email protected]:~$ tar -zxvf cudnn-8.0-linux-x64-v7.tgz
#切換到剛剛解壓出來的資料夾路徑
cd cuda
#複製include裡的標頭檔案（記得轉到include檔案裡執行下面命令）
sudo cp include/cudnn.h /usr/local/cuda/include/
#複製lib64下的lib檔案到cuda安裝路徑下的lib64（記得轉到lib64檔案裡執行下面命令）
sudo cp lib* /usr/local/cuda/lib64/
#設定許可權
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*
#======更新軟連線======
cd /usr/local/cuda/lib64/
sudo rm -rf libcudnn.so libcudnn.so.7 #刪除原有動態檔案，版本號注意變化，可在cudnn的lib64資料夾中檢視
sudo ln -s libcudnn.so.7.0.5 libcudnn.so.7 #生成軟銜接（注意這裡要和自己下載的cudnn版本對應，可以在/usr/local/cuda/lib64下檢視自己libcudnn的版本）
sudo ln -s libcudnn.so.7 libcudnn.so #生成軟連結
sudo ldconfig -v #立刻生效
備註：上面的軟連線的版本號要根據自己實際下載的cudnn的lib版本號
最後我們看看驗證安裝cudnn後cuda是否依舊可用
[email protected]:/usr/local/cuda/lib64$ nvcc --version # or nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Tue_Jan_10_13:22:03_CST_2017
Cuda compilation tools, release 8.0, V8.0.61
4.3 檢驗cuDNN是否安裝成功
到目前為止，cuDNN已經安裝完了，但是，是否成功安裝，我們可以通過cuDNN sample測試一下(https://developer.nvidia.com/rdp/cudnn-archive 頁面中找到對應的cudnn版本，裡面有 cuDNN v5 Code Samples，點選該連結下載即可，版本可能不一樣，下載最新的就行)
下載完，轉到解壓出的目錄下的mnistCUDNN
# Copy the cuDNN sample to a writable path.
$cp -r /usr/src/cudnn_samples_v7/ $HOME
# Go to the writable path
$ cd $HOME/cudnn_samples_v7/mnistCUDNN
# Compile the mnistCUDNN sample
$make clean
$make
Run the mnistCUDNN sample
[email protected]:~/cudnn_samples_v7/mnistCUDNN$ ./mnistCUDNN
cudnnGetVersion() : 7005 , CUDNN_VERSION from cudnn.h : 7005 (7.0.5)
Host compiler version : GCC 5.4.0
There are 1 CUDA capable devices on your machine :
device 0 : sms 13 Capabilities 3.5, SmClock 705.5 Mhz, MemSize (Mb) 4742, MemClock 2600.0 Mhz, Ecc=1, boardGroupID=0
Using device 0
Testing single precision
Loading image data/one_28x28.pgm
Performing forward propagation ...
Testing cudnnGetConvolutionForwardAlgorithm ...
Fastest algorithm is Algo 2
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.041376 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.079680 time requiring 57600 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.092288 time requiring 100 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.153120 time requiring 203008 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.172800 time requiring 207360 memory
Resulting weights from Softmax:
0.0000000 0.9999399 0.0000000 0.0000000 0.0000561 0.0000000 0.0000012 0.0000017 0.0000010 0.0000000
Loading image data/three_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000000 0.0000000 0.9999288 0.0000000 0.0000711 0.0000000 0.0000000 0.0000000 0.0000000
Loading image data/five_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 0.9999820 0.0000154 0.0000000 0.0000012 0.0000006
Result of classification: 1 3 5
Test passed!
Testing half precision (math in single precision)
Loading image data/one_28x28.pgm
Performing forward propagation ...
Testing cudnnGetConvolutionForwardAlgorithm ...
Fastest algorithm is Algo 2
Testing cudnnFindConvolutionForwardAlgorithm ...
^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.061024 time requiring 0 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.061312 time requiring 100 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.086560 time requiring 28800 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.172704 time requiring 203008 memory
^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.186944 time requiring 207360 memory
Resulting weights from Softmax:
0.0000001 1.0000000 0.0000001 0.0000000 0.0000563 0.0000001 0.0000012 0.0000017 0.0000010 0.0000001
Loading image data/three_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000000 0.0000000 1.0000000 0.0000000 0.0000714 0.0000000 0.0000000 0.0000000 0.0000000
Loading image data/five_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 1.0000000 0.0000154 0.0000000 0.0000012 0.0000006
Result of classification: 1 3 5
Test passed!
Test passed! 至此，cuDNN已經成功安裝了
5 Anaconda3
Anaconda是python的一個科學計算髮行版，內建了數百個python經常會使用的庫，很多是TensorFlow的依賴庫。安裝好Anaconda可以提供一個好的環境直接安裝TensorFlow。
bash Anaconda3-4.2.0-Linux-x86_64.sh
安裝anaconda，回車後，是許可檔案，接收許可。直接回車即可。最後會詢問是否把anaconda的bin新增到使用者的環境變數中，選擇yes。在終端輸入python發現依然是系統自帶的python版本，這是因為環境變數的更新還沒有生效，命令列輸入如下命令是安裝的anaconda生效。如果conda --version沒有找到任何資訊，說明沒有加入到環境變數沒有，需要手動加入，如圖所示：
[email protected]:/home/zjw# vim ~/.bashrc
[email protected]:/home/zjw# source ~/.bashrc
檢查環境變數是否生效
[email protected]:~$ conda --version
conda 4.4.10
[email protected]:~$ python
Python 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19)
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
python版本為 Anaconda版本而非系統自帶，說明環境變數更新生效
6 Tensorflow
大家可以參考TensorFlow的官方安裝教程（https://www.tensorflow.org/install/），官網提供的了 Pip, Docker, Virtualenv, Anaconda 或 原始碼編譯的方法安裝 TensorFlow，我們這裡主要介紹以Anaconda安裝。其他安裝方式，大家可以到官方安裝教程檢視。
6.1 安裝TensorFlow
通過Anaconda安裝TensorFlow CPU，TensorFlow 的官方下載源現在已經在GitHub上提供了（https://github.com/tensorflow/tensorflow），找到對應的版本號，如圖所示：
6.2 建立一個名為tensorflow的conda環境Python 3.6
#Python 2.7
conda create -n tensorflow python=2.7
#Python 3.4
conda create -n tensorflow python=3.4
#Python 3.5
conda create -n tensorflow python=3.5
#Python 3.6
conda create -n tensorflow python=3.6 #我下的TensorFlow對應的Python是3.6版本，那麼我就使用這行
備註：(根據TensorFlow版本號，一定要設定Python版本號，切記切記切記！！！！！重要的事情說三遍！否則後面會報各種錯的)
建立時出錯
[email protected]:~$ conda create -n tensorflow python=3.6
Solving environment: failed
CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://repo.continuum.io/pkgs/main/linux-64/repoa.json.bz2>
Elapsed: -
...
解決方法：（關閉VPN）
以下是輔助，不一定成功
# 首先新增清華的映象源
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes
6.3 啟用 conda 環境
[email protected]:~$ conda create -n tensorflow pip python=3.6
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.4.10
latest version: 4.5.11
Please update conda by running
$ conda update -n base conda
## Package Plan ##
environment location: /home/zjw/.conda/envs/tensorflow
added / updated specs:
- pip
- python=3.6
The following packages will be downloaded:
package | build
---------------------------|-----------------
xz-5.2.3 | 0 667 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
tk-8.5.18 | 0 1.9 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
wheel-0.29.0 | py36_0 88 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
openssl-1.0.2l | 0 3.2 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
readline-6.2 | 2 606 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
python-3.6.2 | 0 16.5 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
pip-9.0.1 | py36_1 1.7 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
sqlite-3.13.0 | 0 4.0 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
certifi-2016.2.28 | py36_0 216 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
setuptools-36.4.0 | py36_1 563 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
zlib-1.2.11 | 0 109 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
------------------------------------------------------------
Total: 29.3 MB
The following NEW packages will be INSTALLED:
certifi: 2016.2.28-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
openssl: 1.0.2l-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
pip: 9.0.1-py36_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
python: 3.6.2-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
readline: 6.2-2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
setuptools: 36.4.0-py36_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
sqlite: 3.13.0-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
tk: 8.5.18-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
wheel: 0.29.0-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
xz: 5.2.3-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
zlib: 1.2.11-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
Proceed ([y]/n)? y
Downloading and Extracting Packages
xz 5.2.3: ################################################################################### | 100%
tk 8.5.18: ################################################################################## | 100%
wheel 0.29.0: ############################################################################### | 100%
openssl 1.0.2l: ############################################################################# | 100%
readline 6.2: ############################################################################### | 100%
python 3.6.2: ############################################################################### | 100%
pip 9.0.1: ################################################################################## | 100%
sqlite 3.13.0: ############################################################################## | 100%
certifi 2016.2.28: ########################################################################## | 100%
setuptools 36.4.0: ########################################################################## | 100%
zlib 1.2.11: ################################################################################ | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate tensorflow
#
# To deactivate an active environment, use
#
# $ conda deactivate
[email protected]:~$ conda activate tensorflow
source activate tensorflow
6.4 在conda環境中安裝TensorFlow GPU版
因為我們前面選擇了conda環境為Python3.6的，所以我們選擇Python3.6版本的GPU連結地址，進行安裝
#如何進行安裝，我們這裡安裝Python版本為3.6的TensorFlow
sudo pip3 install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0-cp36-cp36m-linux_x86_64.whl
# 備註：連線裡的cpxx和cpxxm的xx是對應Python的版本號#
失敗，我們需要下載GPU版的安裝包，在安裝包下載之後，然後手動進入環境，安裝TensorFlow whl安裝包。
(tensorflow) [email protected]:~$ wget https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0-cp36-cp36m-linux_x86_64.whl
source activate tensorflow #啟用tensorflow環境（這步操作了，就忽略）
(tensorflow) [email protected]:~$ pip install --ignore-installed --upgrade tensorflow_gpu-1.3.0-cp36-cp36m-linux_x86_64.whl
Processing ./tensorflow_gpu-1.3.0-cp36-cp36m-linux_x86_64.whl
Collecting wheel>=0.26 (from tensorflow-gpu==1.3.0)
Downloading https://files.pythonhosted.org/packages/81/30/e935244ca6165187ae8be876b6316ae201b71485538ffac1d718843025a9/wheel-0.31.1-py2.py3-none-any.whl (41kB)
100% |████████████████████████████████| 51kB 96kB/s
Collecting numpy>=1.11.0 (from tensorflow-gpu==1.3.0)
Downloading https://files.pythonhosted.org/packages/22/02/bae88c4aaea4256d890adbf3f7cf33e59a443f9985cf91cd08a35656676a/numpy-1.15.2-cp36-cp36m-manylinux1_x86_64.whl (13.9MB)
100% |████████████████████████████████| 13.9MB 46kB/s
Collecting protobuf>=3.3.0 (from tensorflow-gpu==1.3.0)
Downloading https://files.pythonhosted.org/packages/c2/f9/28787754923612ca9bfdffc588daa05580ed70698add063a5629d1a4209d/protobuf-3.6.1-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)
100% |████████████████████████████████| 1.1MB 110kB/s
Collecting six>=1.10.0 (from tensorflow-gpu==1.3.0)
Downloading https://files.pythonhosted.org/packages/67/4b/141a581104b1f6397bfa78ac9d43d8ad29a7ca43ea90a2d863fe3056e86a/six-1.11.0-py2.py3-none-any.whl
Collecting tensorflow-tensorboard<0.2.0,>=0.1.0 (from tensorflow-gpu==1.3.0)
Retrying (Retry(total=4, connect=None, read=None, redirect=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='pypi.python.org', port=443): Read timed out. (read timeout=15)",)': /simple/tensorflow-tensorboard/
Downloading https://files.pythonhosted.org/packages/93/31/bb4111c3141d22bd7b2b553a26aa0c1863c86cb723919e5bd7847b3de4fc/tensorflow_tensorboard-0.1.8-py3-none-any.whl (1.6MB)
100% |████████████████████████████████| 1.6MB 531kB/s
Collecting setuptools (from protobuf>=3.3.0->tensorflow-gpu==1.3.0)
Downloading https://files.pythonhosted.org/packages/6e/9c/cc2eb661d85f4aa541910af1a72b834a0f5c9209079fcbd1438fa6da17c6/setuptools-40.4.2-py2.py3-none-any.whl (569kB)
100% |████████████████████████████████| 573kB 303kB/s
Collecting werkzeug>=0.11.10 (from tensorflow-tensorboard<0.2.0,>=0.1.0->tensorflow-gpu==1.3.0)
Downloading https://files.pythonhosted.org/packages/20/c4/12e3e56473e52375aa29c4764e70d1b8f3efa6682bef8d0aae04fe335243/Werkzeug-0.14.1-py2.py3-none-any.whl (322kB)
100% |████████████████████████████████| 327kB 329kB/s
Collecting bleach==1.5.0 (from tensorflow-tensorboard<0.2.0,>=0.1.0->tensorflow-gpu==1.3.0)
Downloading https://files.pythonhosted.org/packages/33/70/86c5fec937ea4964184d4d6c4f0b9551564f821e1c3575907639036d9b90/bleach-1.5.0-py2.py3-none-any.whl
Collecting html5lib==0.9999999 (from tensorflow-tensorboard<0.2.0,>=0.1.0->tensorflow-gpu==1.3.0)
Downloading https://files.pythonhosted.org/packages/ae/ae/bcb60402c60932b32dfaf19bb53870b29eda2cd17551ba5639219fb5ebf9/html5lib-0.9999999.tar.gz (889kB)
100% |████████████████████████████████| 890kB 274kB/s
Collecting markdown>=2.6.8 (from tensorflow-tensorboard<0.2.0,>=0.1.0->tensorflow-gpu==1.3.0)
Downloading https://files.pythonhosted.org/packages/7a/fd/e22357c299e93c0bc11ec8ba54e79f98dd568e09adfe9b39d6852c744938/Markdown-3.0-py2.py3-none-any.whl (89kB)
100% |████████████████████████████████| 92kB 331kB/s
Building wheels for collected packages: html5lib
Running setup.py bdist_wheel for html5lib ... done
Stored in directory: /home/zjw/.cache/pip/wheels/50/ae/f9/d2b189788efcf61d1ee0e36045476735c838898eef1cad6e29
Successfully built html5lib
Installing collected packages: wheel, numpy, six, setuptools, protobuf, werkzeug, html5lib, bleach, markdown, tensorflow-tensorboard, tensorflow-gpu
Successfully installed bleach-1.5.0 html5lib-0.9999999 markdown-3.0 numpy-1.15.2 protobuf-3.6.1 setuptools-40.4.2 six-1.11.0 tensorflow-gpu-1.3.0 tensorflow-tensorboard-0.1.8 werkzeug-0.14.1 wheel-0.31.1
You are using pip version 9.0.1, however version 18.0 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
6.5 當你不用 TensorFlow 的時候，關閉環境
source deactivate tensorflow
6.6 安裝成功後,每次使用 TensorFlow 的時候需要啟用 conda 環境（操作步驟2就可以了）
6.7 常見問題
出現“ImportError: libcudnn.so.6: cannot open shared object file: No such file or directory”錯誤資訊
Python 3.6.2 |Continuum Analytics, Inc.| (default, Jul 20 2017, 13:51:3